{"title":"Large-language models: The game-changers for materials science research","authors":"Songlin Yu , Nian Ran , Jianjun Liu","doi":"10.1016/j.aichem.2024.100076","DOIUrl":null,"url":null,"abstract":"<div><p>Large Language Models (LLMs), such as GPT-4, are precipitating a new \"industrial revolution\" by significantly enhancing productivity across various domains. These models encode an extensive corpus of scientific knowledge from vast textual datasets, functioning as near-universal generalists with the ability to engage in natural language communication and exhibit advanced reasoning capabilities. Notably, agents derived from LLMs can comprehend user intent and autonomously design, plan, and utilize tools to execute intricate tasks. These attributes are particularly advantageous for materials science research, an interdisciplinary field characterized by numerous complex and time-intensive activities. The integration of LLMs into materials science research holds the potential to fundamentally transform the research paradigm in this field.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100076"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000344/pdfft?md5=e80906f3aecc3736b5e0dcac5da9017c&pid=1-s2.0-S2949747724000344-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial intelligence chemistry","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949747724000344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Large Language Models (LLMs), such as GPT-4, are precipitating a new "industrial revolution" by significantly enhancing productivity across various domains. These models encode an extensive corpus of scientific knowledge from vast textual datasets, functioning as near-universal generalists with the ability to engage in natural language communication and exhibit advanced reasoning capabilities. Notably, agents derived from LLMs can comprehend user intent and autonomously design, plan, and utilize tools to execute intricate tasks. These attributes are particularly advantageous for materials science research, an interdisciplinary field characterized by numerous complex and time-intensive activities. The integration of LLMs into materials science research holds the potential to fundamentally transform the research paradigm in this field.